scholarly journals An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 602 ◽  
Author(s):  
Rahman ◽  
Avelin ◽  
Kyprianidis
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Near Infrared ◽  
Lignin Content ◽  
Wood Properties ◽  
Kappa Number ◽  
Industrial Control ◽  
Paper Mills ◽  
Predictive Controller ◽  
Pulp Digesters

Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes.

Coordinated Model Predictive Control on Multi-Lane Roads

2019 ◽  
Author(s):  
Nathan Goulet ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Heterogeneous Traffic ◽  
Road Transportation ◽  
The Road ◽  
Energy Saving Potential ◽  
Coordination Scheme ◽  
Predictive Controller

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

scholarly journals Model Predictive Control for First-Order Hyperbolic System Based on Quasi-Shannon Wavelet Basis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1114
Author(s):  
Ling Ai ◽  
Kok Lay Teo ◽  
Liwei Deng ◽  
Desheng Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Heating System ◽  
Order System ◽  
Distributed Parameter ◽  
Wavelet Basis ◽  
First Order ◽  
Forward Difference ◽  
Shannon Wavelet ◽  
Predictive Controller

In this paper, we consider a class of first-order hyperbolic distributed parameter systems. Our focus is on the design of a new class of model predictive control schemes using a quasi-Shannon wavelet basis. First, the first-order hyperbolic distributed parameter system is transformed into an equivalent system using collocation techniques for the approximation of spatial derivatives and Euler forward difference method for the approximation of the time component. Then, a model reduction method is applied to obtain a reduced-order system on which a nonlinear model predictive controller is designed through solving a nonlinear quadratic programming problem with input constraints. For illustration, the temperature control of a flow-control long-duct heating system is considered to be an example. A comparative simulation study is conducted to demonstrate the effectiveness of the proposed method.

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

scholarly journals Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Alfredo Núñez ◽  
Carlos Ocampo-Martinez ◽  
José María Maestre ◽  
Bart De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Reduction Methods ◽  
Predictive Controller ◽  
Online Methods ◽  
Water Tanks ◽  
Predictive Controllers ◽  
Decentralized Model

The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.

scholarly journals Model Predictive Control of NCS with Data Quantization and Bounded Arbitrary Time Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jimin Yu ◽  
Yanan Xie ◽  
Xiaoming Tang
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Infinite Horizon ◽  
Stability Result ◽  
Loop Model ◽  
Unified Framework ◽  
Model Predictive Controller ◽  
Predictive Controller

The model predictive control for constrained discrete time linear system under network environment is considered. The bounded time delay and data quantization are assumed to coexist in the data transmission link from the sensor to the controller. A novel NCS model is specially established for the model predictive control method, which casts the time delay and data quantization into a unified framework. A stability result of the obtained closed-loop model is presented by applying the Lyapunov method, which plays a key role in synthesizing the model predictive controller. The model predictive controller, which parameterizes the infinite horizon control moves into a single state feedback law, is provided which explicitly considers the satisfaction of input and state constraints. Two numerical examples are given to illustrate the effectiveness of the derived method.

An active fault-tolerant control strategy of aircraft engines based on multi-model predictive control

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xian Du ◽  
Yan-Hua Ma
Keyword(s):  
High Pressure ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Active Fault ◽  
Fault Tolerant ◽  
Control Sample ◽  
Fault Model ◽  
Aircraft Engines ◽  
Component Level ◽  
Predictive Controller

AbstractIn order to mitigate or even eliminate the adverse effects caused by typical components faults of aircraft engines, an active fault-tolerant strategy based on multi-model predictive control is proposed, which consists of a pre-established multi-model library, a judgement module, and corresponding predictive controllers with smooth transition switching logic. Multiple dynamic nonlinear or linear models are firstly established by means of system identification methods, based on the component-level nonlinear engine model or historical data in faults cases. The judgement module is utilized to online compare the engine measured outputs with that of all models in the pattern library and select the best matched dynamic model on the basis of outputs error quadratic performance index, thus determining the most appropriate predictive controller for the next control sample period. When a certain fault occurs, the fault model in the library could be identified and fault-model based predictive controller is activated. Finally, two kinds of pre-considered high-pressure compressor and high-pressure turbine component-level faults are taken as an example to design the active fault-tolerant controller. Simulation results show that the judgement module owns the ability to sense the fault and gives smooth switching signal to the suitable predictive controller, verifying the effectiveness of the proposed technique.

scholarly journals Incremental State-Space Model Predictive Control of a Fresnel Solar Collector Field

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Eduardo Camacho ◽  
Antonio Gallego ◽  
Adolfo Sanchez ◽  
Manuel Berenguel
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
State Space ◽  
Predictive Control ◽  
State Space Model ◽  
Distributed Parameter ◽  
Space Model ◽  
Luenberger Observer ◽  
Predictive Controller ◽  
Nonlinear Distributed Parameter Model

Model predictive control has been demonstrated to be one of the most efficient control techniques for solar power systems. An incremental offset-free state-space Model Predictive Controller (MPC) is developed for the Fresnel collector field located at the solar cooling plant installed on the roof of the Engineering School of Sevilla. A robust Luenberger observer is used for estimating the states of the plant which cannot be measured. The proposed strategy is tested on a nonlinear distributed parameter model of the Fresnel collector field. Its performance is compared to that obtained with a gain-scheduling generalized predictive controller. A real test carried out at the real plant is presented, showing that the proposed strategy achieves a very good performance.

scholarly journals Robust Output Model Predictive Control of an Unstable Rijke Tube

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Fabian Jarmolowitz ◽  
Christopher Groß-Weege ◽  
Thomas Lammersen ◽  
Dirk Abel
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Rijke Tube ◽  
Guaranteed Stability ◽  
Predictive Controller ◽  
Linear State ◽  
Gain Scheduled

This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC). As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

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